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Record W3118075159 · doi:10.11575/prism/38494

“Be Professional, Private and Pleasant”: The Conscious and Unconscious Gendering of Campaign Messages in Canadian and Australian Local Elections

2020· dissertation· en· W3118075159 on OpenAlexaboutno aff
Julie Lynn Croskill

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsUnconscious mindPolitical scienceConsciousnessMedia studiesSociologyPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

This dissertation examines Australian and Canadian local campaigns to investigate the extent to which gender and gendered stereotypes consciously affect candidates’ campaign messaging. The data for this study was gathered via in-depth interviews with 92 candidates who contested elections at the state/provincial level between 2010 and 2013. The data collected during the interviews included information on how candidates presented themselves in terms of their appearance, qualifications, character traits and family life; the issues that they highlighted in their local campaigns; the voters they targeted and strategies to connect with them; and information about their opponent relationships such as whether they formed civility pacts, employed negative attack messaging and how they responded if they were negatively campaigned against. The main conclusion is that gender affects political campaigns. Women’s campaign messaging looks different from men’s campaign messaging in several ways. For example, women are less likely to share personal information about themselves and their families and less likely to target an opponent with negative attack messages despite being more likely to be the target of such attacks. Among the most competitive women candidates, the differences found between their campaigns, and men’s campaigns, regardless of competitiveness, started to diminish. In terms of understanding why campaigns are gendered, there was minimal evidence detected that candidates consciously adjusted their messaging in response to what they perceived to be either voter-held or self-held beliefs about gendered stereotypes. Thus, gendered campaign messaging is the result of unconscious gender role stereotypes. By and large, women candidates did not cue gender in their local campaigns by highlighting women’s issues in their messaging, or by appealing to voters to support a woman candidate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.008
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.378
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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